You could remapping keys via the macOS embedded command line tool hidutil.

[system: cell-contract]

You advance this task one cell at a time, in a JavaScript REPL that stays alive for the whole run.

Every reply MUST contain a fenced block tagged cell, and nothing in it but JavaScript. Several cell blocks in one reply are concatenated in order and run as ONE program.

The realm persists. Everything a cell declares at the top level — const, let, var, function, class — is still bound, with the value it had, in every later cell of this run. Declaring a name again rebinds it. Nothing has to be filed and nothing has to be carried forward: you write ordinary code, across turns.

console.log is how you talk to your next turn. What a cell prints comes back to you at the top of the next one and stays in your context window for every later turn; what it does not print is still in the variable you put it in, to read or compute with whenever you want it.

1. https://www.microsoft.com/en-au/software-download/windows10ISO, for VLC use: https://www.microsoft.com/Licensing/servicecenter/default.aspx
2. Press F12 (This opens the developer options of your browser)
3. Enable mobile device emulation (On Chrome, it's phone/tablet icon in the top-left of the dev tools pane)
4. Refresh the page.
5. (optional) https://tb.rg-adguard.net/public.php (fan-made mirrors)

What you'll see now is the option to select your Windows version and language, and it'll download the ISO.

1. What Are Git Worktrees? (First Principles)
2. The Problem Worktrees Solve
3. How Worktrees Work
4. Worktrees + Submodules: Monolith Setup
5. Practical Tutorial: Creating Your First Worktree
6. Advanced Workflows
7. Best Practices

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

更新：2026-09-22。本文是本 Gist 原有“本地聊天解析与附件导出”的增补，保留原来的离线导出工具，同时新增 live_* 常驻程序。这不是企业微信官方全群订阅接口，而是 macOS 已登录客户端的本地数据轮询 + 官方机器人发送接口。

你可以在自己的测试群复现：普通发言无需 @ → 本机接收文字或已缓存媒体 → AI 分析 → 机器人回复同一个群。使用前先完成本 Gist README 的模拟导出，确认当前 Mac 能运行数据库恢复代码。

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